Decomposition of the European GDP based on Singular Spectrum Analysis

Costas Leon

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper, the Singular Spectrum Analysis (SSA), a relatively new tool originated in natural sciences, for orthogonal decomposition of time series, is presented and applied in the European real, seasonally unadjusted quarterly GDP for the period 1995 - 2010. SSA is suitable for short and noisy time series, properties that characterize many macroeconomic time series. In this paper, I decompose the GDP in trend, cycle, seasonals and noise components. There are significant similarities but also some differences between the SSA-based filter and the other well-known macroeconomic filters. These differences are shown here by means of correlation matrices and spectral measures. Although SSA is a method that only very recently has been introduced in macroeconomics, its use in the natural sciences for more than three decades, makes it a serious candidate for tackling macroeconomic issues such as filtering, denoising and smoothing.

Keywords: Macroeconomics; economic fluctuations; business cycle; dynamical systems; spectral methods; singular spectrum analysis. (search for similar items in EconPapers)
JEL-codes: C1 C15 C4 C5 E3 E32 E37 (search for similar items in EconPapers)
Date: 2015-07-28
New Economics Papers: this item is included in nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/65812/1/MPRA_paper_65812.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:65812

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().